OpenRaiser/Intern-Atlas huggingface dataset:
Effective Data Mining Technique for Classification Cancers via Mutations in Gene using Neural Network
Venue: arXiv cs.LG 2016
Year: 2016

The prediction plays the important role in detecting efficient protection and therapy of cancer. The prediction of mutations in gene needs a diagnostic and classification, which is based on the whole database (big dataset), to reach sufficient accuracy results. Since the tumor suppressor P53 is approximately about fifty percentage of all human tumors because mutations that occur in the TP53 gene into the cells. So, this paper is applied on tumor p53, where the problem is there are several primitive databases (excel database) contain datasets of TP53 gene with its tumor protein p53, these databases are rich datasets that cover all mutations and cause diseases (cancers). But these Data Bases cannot reach to predict and diagnosis cancers, i.e. the big datasets have not efficient Data Mining method, which can predict, diagnosis the mutation, and classify the cancer of patient. The goal of this paper to reach a Data Mining technique, that employs neural network, which bases on the big datasets. Also, offers friendly predictions, flexible, and effective classified cancers, in order to overcome the previous techniques drawbacks. This proposed technique is done by using two approaches, first, bioinformatics techniques by using BLAST, CLUSTALW, etc, in order to know if there are malignant mutations or not. The second, data mining by using neural network; it is selected (12) out of (53) TP53 gene database fields. To clarify, one of these 12 fields (gene location field) did not exists in TP53 gene database; therefore, it is added to the database of TP53 gene in training and testing back propagation algorithm, in order to classify specifically the types of cancers. Feed Forward Back Propagation supports this Data Mining method with data training rate (1) and Mean Square Error (MSE) (0.00000000000001). This effective technique allows in a quick, accurate and easy way to classify the type of cancer.
A Formal, Resource Consumption-Preserving Translation of Actors to Haskell
Venue: arXiv cs.PL 2016
Year: 2016

We present a formal translation of an actor-based language with cooperative scheduling to the functional language Haskell. The translation is proven correct with respect to a formal semantics of the source language and a high-level operational semantics of the target, i.e. a subset of Haskell. The main correctness theorem is expressed in terms of a simulation relation between the operational semantics of actor programs and their translation. This allows us to then prove that the resource consumption is preserved over this translation, as we establish an equivalence of the cost of the original and Haskell-translated execution traces.
Temporal Attention Model for Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

Attention-based Neural Machine Translation (NMT) models suffer from attention deficiency issues as has been observed in recent research. We propose a novel mechanism to address some of these limitations and improve the NMT attention. Specifically, our approach memorizes the alignments temporally (within each sentence) and modulates the attention with the accumulated temporal memory, as the decoder generates the candidate translation. We compare our approach against the baseline NMT model and two other related approaches that address this issue either explicitly or implicitly. Large-scale experiments on two language pairs show that our approach achieves better and robust gains over the baseline and related NMT approaches. Our model further outperforms strong SMT baselines in some settings even without using ensembles.
StegIbiza: New Method for Information Hiding in Club Music
Venue: arXiv cs.CR 2016
Year: 2016

In this paper a new method for information hiding in club music is introduced. The method called StegIbiza is based on using the music tempo as a carrier. The tempo is modulated by hidden messages with a 3-value coding scheme, which is an adoption of Morse code for StegIbiza. The evaluation of the system was performed for several music samples (with and without StegIbiza enabled) on a selected group of testers who had a music background. Finally, for the worst case scenario, none of them could identify any differences in the audio with a 1% margin of changed tempo.
Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
Venue: arXiv cs.CL 2016
Year: 2016

Current approaches to learning vector representations of text that are compatible between different languages usually require some amount of parallel text, aligned at word, sentence or at least document level. We hypothesize however, that different natural languages share enough semantic structure that it should be possible, in principle, to learn compatible vector representations just by analyzing the monolingual distribution of words. In order to evaluate this hypothesis, we propose a scheme to map word vectors trained on a source language to vectors semantically compatible with word vectors trained on a target language using an adversarial autoencoder. We present preliminary qualitative results and discuss possible future developments of this technique, such as applications to cross-lingual sentence representations.
Regression Models for Output Prediction of Thermal Dynamics in Buildings
Venue: arXiv cs.SY 2016
Year: 2016

Standard (black-box) regression models may not necessarily suffice for accurate identification and prediction of thermal dynamics in buildings. This is particularly apparent when either the flow rate or the inlet temperature of the thermal medium varies significantly with time. To this end, this paper analytically derives, using physical insight, and investigates linear regression models with nonlinear regressors for system identification and prediction of thermal dynamics in buildings. Comparison is performed with standard linear regression models with respect to both a) identification error, and b) prediction performance within a model-predictive-control implementation for climate control in a residential building. The implementation is performed through the EnergyPlus building simulator and demonstrates that a careful consideration of the nonlinear effects may provide significant benefits with respect to the power consumption.
Uncertainty-Driven Black-Box Test Data Generation
Venue: arXiv cs.SE 2016
Year: 2016

We can never be certain that a software system is correct simply by testing it, but with every additional successful test we become less uncertain about its correctness. In absence of source code or elaborate specifications and models, tests are usually generated or chosen randomly. However, rather than randomly choosing tests, it would be preferable to choose those tests that decrease our uncertainty about correctness the most. In order to guide test generation, we apply what is referred to in Machine Learning as "Query Strategy Framework": We infer a behavioural model of the system under test and select those tests which the inferred model is "least certain" about. Running these tests on the system under test thus directly targets those parts about which tests so far have failed to inform the model. We provide an implementation that uses a genetic programming engine for model inference in order to enable an uncertainty sampling technique known as "query by committee", and evaluate it on eight subject systems from the Apache Commons Math framework and JodaTime. The results indicate that test generation using uncertainty sampling outperforms conventional and Adaptive Random Testing.
On the limitations of deterministic de-randomizations for online bipartite matching and max-sat
Venue: arXiv cs.DS 2016
Year: 2016

The surprising results of Karp, Vazirani and Vazirani and (respectively) Buchbinder et al are examples where rather simple randomizations provide provably better approximations than the corresponding deterministic counterparts for online bipartite matching and (respectively) unconstrained non-monotone submodular maximization. We show that seemingly strong extensions of the deterministic online computation model can at best match the performance of naive randomization. More specifically, for bipartite matching, we show that in the priority model (allowing very general ways to order the input stream), we cannot improve upon the trivial 1/2-approximation achieved by any greedy maximal matching algorithm and likewise cannot improve upon this approximation by any log n/log log n number of online algorithms running in parallel. The latter result yields an improved log log n - log log log n lower bound for the number of advice bits needed. For max-sat, we adapt the recent de-randomization approach of Buchbinder and Feldman applied to the Buchbinbder et al algorithm for max-sat to obtain a deterministic 3/4-approximation algorithm using width 2n parallelism. In order to improve upon this approximation, we show that exponential width parallelism of online algorithms is necessary (in a model that is more general than what is needed for the width 2n algorithm).
Fractional Calculus In Image Processing: A Review
Venue: arXiv cs.CV 2016
Year: 2016

Over the last decade, it has been demonstrated that many systems in science and engineering can be modeled more accurately by fractional-order than integer-order derivatives, and many methods are developed to solve the problem of fractional systems. Due to the extra free parameter order, fractional-order based methods provide additional degree of freedom in optimization performance. Not surprisingly, many fractional-order based methods have been used in image processing field. Herein recent studies are reviewed in ten sub-fields, which include image enhancement, image denoising, image edge detection, image segmentation, image registration, image recognition, image fusion, image encryption, image compression and image restoration. In sum, it is well proved that as a fundamental mathematic tool, fractional-order derivative shows great success in image processing.
Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More
Venue: arXiv cs.DS 2016
Year: 2016

In this paper, we provide faster algorithms for computing various fundamental quantities associated with random walks on a directed graph, including the stationary distribution, personalized PageRank vectors, hitting times, and escape probabilities. In particular, on a directed graph with $n$ vertices and $m$ edges, we show how to compute each quantity in time $\tilde{O}(m^{3/4}n+mn^{2/3})$, where the $\tilde{O}$ notation suppresses polylogarithmic factors in $n$, the desired accuracy, and the appropriate condition number (i.e. the mixing time or restart probability). Our result improves upon the previous fastest running times for these problems; previous results either invoke a general purpose linear system solver on a $n\times n$ matrix with $m$ non-zero entries, or depend polynomially on the desired error or natural condition number associated with the problem (i.e. the mixing time or restart probability). For sparse graphs, we obtain a running time of $\tilde{O}(n^{7/4})$, breaking the $O(n^{2})$ barrier of the best running time one could hope to achieve using fast matrix multiplication. We achieve our result by providing a similar running time improvement for solving directed Laplacian systems, a natural directed or asymmetric analog of the well studied symmetric or undirected Laplacian systems. We show how to solve such systems in time $\tilde{O}(m^{3/4}n+mn^{2/3})$, and efficiently reduce a broad range of problems to solving $\tilde{O}(1)$ directed Laplacian systems on Eulerian graphs. We hope these results and our analysis open the door for further study into directed spectral graph theory.
Architecture Diagrams: A Graphical Language for Architecture Style Specification
Venue: arXiv cs.SE 2016
Year: 2016

Architecture styles characterise families of architectures sharing common characteristics. We have recently proposed configuration logics for architecture style specification. In this paper, we study a graphical notation to enhance readability and easiness of expression. We study simple architecture diagrams and a more expressive extension, interval architecture diagrams. For each type of diagrams, we present its semantics, a set of necessary and sufficient consistency conditions and a method that allows to characterise compositionally the specified architectures. We provide several examples illustrating the application of the results. We also present a polynomial-time algorithm for checking that a given architecture conforms to the architecture style specified by a diagram.
Worlds of Events: Deduction with Partial Knowledge about Causality
Venue: arXiv cs.DC 2016
Year: 2016

Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of keeping large quantities of metadata; even more challenging when dealing with resource-limited devices. In this paper, we focus on keeping partial knowledge on causality and address deduction from that knowledge. We provide the first proof-theoretic causality modelling for distributed partial knowledge. We prove computability and consistency results. We also prove that the partial knowledge gives rise to a weaker model than classical causality. We provide rules for offline deduction about causality and refute some related folklore. We define two notions of forward and backward bisimilarity between devices, using which we prove two important results. Namely, no matter the order of addition/removal, two devices deduce similarly about causality so long as: (1) the same causal information is fed to both. (2) they start bisimilar and erase the same causal information. Thanks to our establishment of forward and backward bisimilarity, respectively, proofs of the latter two results work by simple induction on length.
A machine learning method for the large-scale evaluation of urban visual environment
Venue: arXiv cs.CV 2016
Year: 2016

Given the size of modern cities in the urbanising age, it is beyond the perceptual capacity of most people to develop a good knowledge about the beauty and ugliness of the city at every street corner. Correspondingly, for planners, it is also difficult to accurately answer questions like 'where are the worst-looking places in the city that regeneration should give first consideration', or 'in the fast urbanising cities, how is the city appearance changing', etc. To address this issue, we here present a computer vision method for the large-scale and automatic evaluation of the urban visual environment, by leveraging state-of-the-art machine learning techniques and the wide-coverage street view images. From the various factors that are at work, we choose two key features, the visual quality of street facade and the continuity of street wall, as the starting point of this line of analysis. In order to test the validity of this method, we further compare the machine ratings with ratings collected on site from 752 passers-by on fifty-six locations. We show that the machine learning model can produce a good estimation of people's real visual experience, and it holds much potential for various tasks in terms of urban design evaluation, culture identification, etc.
Solving Visual Madlibs with Multiple Cues
Venue: arXiv cs.CV 2016
Year: 2016

This paper focuses on answering fill-in-the-blank style multiple choice questions from the Visual Madlibs dataset. Previous approaches to Visual Question Answering (VQA) have mainly used generic image features from networks trained on the ImageNet dataset, despite the wide scope of questions. In contrast, our approach employs features derived from networks trained for specialized tasks of scene classification, person activity prediction, and person and object attribute prediction. We also present a method for selecting sub-regions of an image that are relevant for evaluating the appropriateness of a putative answer. Visual features are computed both from the whole image and from local regions, while sentences are mapped to a common space using a simple normalized canonical correlation analysis (CCA) model. Our results show a significant improvement over the previous state of the art, and indicate that answering different question types benefits from examining a variety of image cues and carefully choosing informative image sub-regions.
Bird detection in audio: a survey and a challenge
Venue: arXiv cs.SD 2016
Year: 2016

Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly large datasets, this detection is often manual or semi-automatic, requiring manual tuning/postprocessing. We review the state of the art in automatic bird sound detection, and identify a widespread need for tuning-free and species-agnostic approaches. We introduce new datasets and an IEEE research challenge to address this need, to make possible the development of fully automatic algorithms for bird sound detection.
Finding Large Set Covers Faster via the Representation Method
Venue: arXiv cs.DS 2016
Year: 2016

The worst-case fastest known algorithm for the Set Cover problem on universes with $n$ elements still essentially is the simple $O^*(2^n)$-time dynamic programming algorithm, and no non-trivial consequences of an $O^*(1.01^n)$-time algorithm are known. Motivated by this chasm, we study the following natural question: Which instances of Set Cover can we solve faster than the simple dynamic programming algorithm? Specifically, we give a Monte Carlo algorithm that determines the existence of a set cover of size $σn$ in $O^*(2^{(1-Ω(σ^4))n})$ time. Our approach is also applicable to Set Cover instances with exponentially many sets: By reducing the task of finding the chromatic number $χ(G)$ of a given $n$-vertex graph $G$ to Set Cover in the natural way, we show there is an $O^*(2^{(1-Ω(σ^4))n})$-time randomized algorithm that given integer $s=σn$, outputs NO if $χ(G) > s$ and YES with constant probability if $χ(G)\leq s-1$. On a high level, our results are inspired by the `representation method' of Howgrave-Graham and Joux~[EUROCRYPT'10] and obtained by only evaluating a randomly sampled subset of the table entries of a dynamic programming algorithm.
Sex, drugs, and violence
Venue: arXiv cs.CL 2016
Year: 2016

Automatically detecting inappropriate content can be a difficult NLP task, requiring understanding context and innuendo, not just identifying specific keywords. Due to the large quantity of online user-generated content, automatic detection is becoming increasingly necessary. We take a largely unsupervised approach using a large corpus of narratives from a community-based self-publishing website and a small segment of crowd-sourced annotations. We explore topic modelling using latent Dirichlet allocation (and a variation), and use these to regress appropriateness ratings, effectively automating rating for suitability. The results suggest that certain topics inferred may be useful in detecting latent inappropriateness -- yielding recall up to 96% and low regression errors.
A Machine Learning Based Forwarding Algorithm Over Cognitive Radios in Wireless Mesh Networks
Venue: arXiv cs.NI 2016
Year: 2016

Wireless Mesh Networks improve their capacities by equipping mesh nodes with multi-radios tuned to non-overlapping channels. Hence the data forwarding between two nodes has multiple selections of links and the bandwidth between the pair of nodes varies dynamically. Under this condition, a mesh node adopts machine learning mechanisms to choose the possible best next hop which has maximum bandwidth when it intends to forward data. In this paper, we present a machine learning based forwarding algorithm to let a forwarding node dynamically select the next hop with highest potential bandwidth capacity to resume communication based on learning algorithm. Key to this strategy is that a node only maintains three past status, and then it is able to learn and predict the potential bandwidth capacities of its links. Then, the node selects the next hop with potential maximal link bandwidth. Moreover, a geometrical based algorithm is developed to let the source node figure out the forwarding region in order to avoid flooding. Simulations demonstrate that our approach significantly speeds up the transmission and outperforms other peer algorithms.
Constrained Submodular Maximization: Beyond 1/e
Venue: arXiv cs.DS 2016
Year: 2016

In this work, we present a new algorithm for maximizing a non-monotone submodular function subject to a general constraint. Our algorithm finds an approximate fractional solution for maximizing the multilinear extension of the function over a down-closed polytope. The approximation guarantee is 0.372 and it is the first improvement over the 1/e approximation achieved by the unified Continuous Greedy algorithm [Feldman et al., FOCS 2011].
Transactive Energy Auction with Hidden User Information in Microgrid
Venue: arXiv cs.GT 2016
Year: 2016

This research proposes a novel auction mechanism for transactive energy exchange between buyers and sellers, modeled as agents in a microgrid. The mechanism is implemented by a separate microgrid controller (MC) agent, and requires big data flow with the other agents through an iterative bidding process. Although private user information remains hidden to the MC, a theoretical analysis shows that under the assumption of convexity of the agents utilities, the proposed auction is still able to maximize the social welfare (SW), i.e. the aggregate utilities of the agents. In addition, it is shown that the mechanism exhibits the key desirable features of individual rationality and weak budget balance, guaranteeing that neither the payoff to any agent nor the net monetary revenue after termination, is negative. The proposed approach also incorporates a mechanism to redistribute the sellers shares in a fair manner. As an example, a maximum entropy based fair redistribution scheme is addressed. The theoretical analysis reported here is accompanied by extensive set of simulations that illustrate the various aspects of the proposed mechanism.
Applying Deep Learning to Basketball Trajectories
Venue: arXiv cs.NE 2016
Year: 2016

One of the emerging trends for sports analytics is the growing use of player and ball tracking data. A parallel development is deep learning predictive approaches that use vast quantities of data with less reliance on feature engineering. This paper applies recurrent neural networks in the form of sequence modeling to predict whether a three-point shot is successful. The models are capable of learning the trajectory of a basketball without any knowledge of physics. For comparison, a baseline static machine learning model with a full set of features, such as angle and velocity, in addition to the positional data is also tested. Using a dataset of over 20,000 three pointers from NBA SportVu data, the models based simply on sequential positional data outperform a static feature rich machine learning model in predicting whether a three-point shot is successful. This suggests deep learning models may offer an improvement to traditional feature based machine learning methods for tracking data.